FGBNet: A Bio-Subspecies Classification Network with Multi-Level Feature Interaction by Yang Yuan, Danping Huang, Bingbin Cai, Yang Shen, Jingdan Wang, Jiale Xv, Siyu Chen

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Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm by Fang Liu, Xiaoli Liu, Changyou Yin, Hongrong Wang

“…For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. …”
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Data splitting to avoid information leakage with DataSAIL by Roman Joeres, David B. Blumenthal, Olga V. Kalinina

“…Finally, we empirically demonstrate DataSAIL’s impact on evaluating biomedical machine learning models.…”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…To enable efficient and non-destructive prediction of this key quality parameter, this study presents a modeling framework integrating hyperspectral imaging (HSI) technology with a dual-optimization machine learning strategy. …”
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Estimation of Stator Resistance and Rotor Flux Linkage in SPMSM Using CLPSO with Opposition-Based-Learning Strategy by Jian He, Zhao-Hua Liu

“…Furthermore, the proposed parameter estimation model and optimization method are simple and with good accuracy, fast convergence, and easy digital implementation.…”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…The dataset was split into 80% training and 20% testing data (hold-out test) to ensure objective evaluation. Hyperparameter optimization was performed using Bayesian optimization, while model evaluation employed stratified K-fold cross-validation to prevent overfitting. …”
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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

“…According to the random forest model's optimization experiment, the initial unbalanced data's accuracy was 85.26%, and the F1-score was 12.58%. …”
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Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…Model optimization included resampling, feature selection, and hyperparameter tuning. …”
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Multi-Source Data and Semantic Segmentation: Spatial Quality Assessment and Enhancement Strategies for Jinan Mingfu City from a Tourist Perception Perspective by Lin Chen, Xiaoyu Cai, Zhe Liu

“…A synergistic mechanism of machine scoring and manual double-blind scoring is adopted for correlation analysis to determine the impact degree of indicators and optimization strategies. …”
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Student achievement prediction and auxiliary improvement method based on fuzzy decision support system by Xiaoqian Li

“…Compared with traditional machine learning methods, FDSS model has advantages in prediction accuracy and generalization ability. …”
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Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders by Chang Seok Bang

“…Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. …”
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Fast outlier detection for high-dimensional data of wireless sensor networks by Yan Qiao, Xinhong Cui, Peng Jin, Wu Zhang

“…In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. …”
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A state evaluation and fault diagnosis strategy for substation relay protection system integrating multiple intelligent algorithms by Jiajun Wang, Shiyi Jing, Yu Yao, Kunlun Wang, Bo Li

“…This study introduces a new diagnostic framework that combines improved particle swarm optimization, K‐means clustering algorithms, support vector machine (SVM), and learning vector quantization neural networks to provide a comprehensive fault diagnosis and prediction model for relay protection systems. …”
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Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals by Huaixia Shi, Huaqiang Si, Jiyun Qin

“…To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). …”
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Prediction of Chemical Gas Emissions Based on Ecological Environment by Guobin Chen, Shijin Li

“…This paper proposes a gray wolf optimization algorithm based on chaotic search strategy combined with extreme learning machine to predict chemical emission gases, taking a 330 MW pulverized coal-fired boiler as a test object and establishing chemical emissions of CNGWO-ELM. …”
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An Information Granulated Based SVM Approach for Anomaly Detection of Main Transformers in Nuclear Power Plants by Wenmin Yu, Ren Yu, Cheng Li

“…A condition prediction method based on the online support vector machine (SVM) regression model is proposed, with the input data being preprocessed using the information granulation method, and the parameters of the model are optimized using the particle swarm optimization (PSO) algorithm. …”
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Diabetes-focused food recommender system (DFRS) to enabling digital health. by Esmael Ahmed, Mohammed Oumer, Medina Hassan

“…The methodology involves data collection from diverse patient profiles and model development using Graph Neural Networks (GNN) and other machine learning techniques. …”
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基于QPSO-SVM的轴承故障诊断方法 by 杨光春, 蹇清平

“…Due to the importance of rolling bearing as one of the most widely used in rotating machines,bearing failures have adverse effects on the safe operation of the equipment,in order to diagnosing the fault of rolling bearing effectively,a fault diagnosis model of support vector machine（SVM）optimized by quantum particle swarm optimization（QPSO）algorithm is proposed.First,fault vibration signals are decomposed into several intrinsic mode functions（IMFs）using empirical mode decomposition（EMD）method,then the instantaneous amplitudes of the IMFs that have the fault characteristics are extracted and regarded as the features vector,finally the SVM model optimized by QPSO is used for the failure mode identification.The experimental results indicate that the proposed bearing fault diagnosis method has good capability for adaptive features extraction as well as high fault diagnostic accuracy.…”
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Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost by HEI Xinhong, GAO Miao, ZHANG Kuan, FEI Rong, QIU Yuan, JI Wenjiang

“…The Nadam optimization algorithm was used to optimize the components of the TimeGAN model, that was, the Nadam-TimeGAN model was constructed for data expansion. …”
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Berth Allocation and Quay Crane Scheduling in Port Operations: A Systematic Review by Ndifelani Makhado, Thulane Paepae, Matthews Sejeso, Charis Harley

“…Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. …”
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